Modeling the effect of task complexity on stated choice behavior allowing for differential sensitivity: comparison between utility and regret models

The aim of this paper is to better understand the effect of task complexity in experimental design on individual choice behavior. Although the scale factor in discrete choice models is generally normalized, studies have consistently pointed out that the imposed randomness on individual choice behavior varies as a function of task complexity. Therefore, several researchers incorporated the effect of task complexity by parameterizing the scale factor as an exponential function of task complexity. Consequently, choice behavior becomes more random with increased task complexity. Moreover, sensitivity to task complexity increases at an exponential rate with decreasing task complexity. In this paper, to relax these rigorous assumptions, the authors propose an alternative function that can be viewed as a generalization of the commonly used exponential function. The newly suggested approach is examined for both conventional utility-maximization and regret-minimization models. To assess the empirical performance of the proposed methodology, they designed a stated preference survey with seven task scenarios, differing in their degree of complexity. Estimation results evidence that the generalized parameterization of the scale factor as a function of task complexity better represents individuals’ stated choices in both utility and regret models.